Cause or Correlation?: Three centuries ago, Bishop Berkeley's 1710 classic “A treatise on the nature of human knowledge,” first spelled out the “correlation vs. causation” dilemma.Sugiharaet al.(p.496, published online 20 September) present an approach to this conundrum, and extend current discussions about causation to dynamic systems with weak to moderate coupling (such as ecosystems). The resulting method, convergent cross mapping can detect causal linkages between time series.

A central challenge in microbial ecology is to understand the underlying mechanisms driving community assembly, particularly in the continuum of species sorting and dispersal limitation. However, little is known about the relative importance of species sorting and dispersal limitation in shaping marine microbial communities; especially, how they are related to organism types/traits and water depth. Here, we used variation partitioning and null model analysis to compare mechanisms driving bacterial and protist metacommunity dynamics at the basin scale in the East China Sea, based on MiSeq paired-end sequencing of 16S ribosomal DNA (rDNA) and 18S rDNA, respectively, in surface, deep chlorophyll maximum and bottom layers. Our analyses indicated that protist communities were governed more strongly by species sorting relative to dispersal limitation than were bacterial communities; this pattern was consistent across the three-depth layers, albeit to different degrees. Furthermore, we detected that bacteria exhibited wider habitat niche breadths than protists, whereas, passive dispersal abilities were not appreciably different between them. Our findings support the ‘size-plasticity’ hypothesis: smaller organisms (bacteria) are less environment filtered than larger organisms (protists), as smaller organisms are more likely to be plastic in metabolic abilities and have greater environmental tolerance.

Motivation: The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community‐led open‐source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene.Main types of variables included: The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record.Spatial location and grain: BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km²(158 cm²) to 100 km²(1,000,000,000,000 cm²).Time period and grain: BioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year.Major taxa and level of measurement: BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates.Software format: .csv and .SQL.

Significance: The conventional parametric approach to modeling relies on hypothesized equations to approximate mechanistic processes. Although there are known limitations in using an assumed set of equations, parametric models remain widely used to test for interactions, make predictions, and guide management decisions. Here, we show that these objectives are better addressed using an alternative equation-free approach, empirical dynamic modeling (EDM). Applied to Fraser River sockeye salmon, EDM models ( i ) recover the mechanistic relationship between the environment and population biology that fisheries models dismiss as insignificant, ( ii ) produce significantly better forecasts compared with contemporary fisheries models, and ( iii ) explicitly link control parameters (spawning abundance) and ecosystem objectives (future recruitment), producing models that are suitable for current management frameworks.